Young-onset Colorectal Cancer Screening Based on Artificial Intelligence

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

11000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-12-01

Study Completion Date

2024-01-25

Brief Summary

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In this study, we aimed to develop, internally and temporally validate the machine learning models to help screen YOCRC bansed on the retrospective extracted Electronic Medical Records (EMR) data.

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Detailed Description

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Diagnosis of young-onset colorectal cancer (YOCRC) has become more common in recent decades. Screening CRC among younger adults still remains a challenge. In this study, We plan to retrospectively extracte the relevant clinical data of young individuals who underwent colonoscopy from 2013 to 2022 using Electronic Medical Record (EMR). Multiple supervised machine learning techniques will be applied to distinguish YOCRC and non-YOCRC individuals, the above classifiers will be trained and internally validated in the training dataset and internal validation dataset admitted between 2013 and 2021, respectively. We will also assess the temporal external validity of the classifiers based on the admissions from 2022.

Conditions

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Colorectal Cancer

Study Design

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Observational Model Type

OTHER

Study Time Perspective

RETROSPECTIVE

Study Groups

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Patients with young-onset colorectal cancer

Patients were diagnosed with young-onset colorectal cancer after receiving colonoscopy examination.

Using routine clinical data and machine learning models.

Intervention Type DIAGNOSTIC_TEST

This study used clinical data and machine learning model to screen young-onset colorectal cancer.

Patients without young-onset colorectal cancer

Patients were ruled out young-onset colorectal cancer after receiving colonoscopy examination.

Using routine clinical data and machine learning models.

Intervention Type DIAGNOSTIC_TEST

This study used clinical data and machine learning model to screen young-onset colorectal cancer.

Interventions

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Using routine clinical data and machine learning models.

This study used clinical data and machine learning model to screen young-onset colorectal cancer.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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Inclusion Criteria

* Newly diagnosed with CRC (YOCRC group)
* Age at 18-49 when diagnosis (YOCRC group)
* Never received any CRC-related treatment (YOCRC group)
* No CRC confirmed by colonoscopy or pathology (non-YOCRC group)
* Age at 18-49 (non-YOCRC group)

Exclusion Criteria

* Hospital stay less than 24 hours or with incomplete Complete Blood Count
* Patients with inflammatory bowel disease or hereditary CRC syndromes
* History of other types of primary malignant tumor and other reasons that made them unsuitable for enrollment

Minimum Eligible Age

18 Years

Maximum Eligible Age

49 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes